acdf cross validation analysis accurracy kaze feature detector k means clustering matlab software vision system toolbox  (MathWorks Inc)

Journal: Diagnostics

Article Title: Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future

doi: 10.3390/diagnostics13142429

Figure Lengend Snippet: Studies evaluating machine learning algorithms used for neurosurgical outcome prediction.

Article Snippet: Huang et al., 2019 [ ] , Identification of implanted spinal hardware , AP film cervical radiography after ACDF , Cross-validation analysis Accurracy , KAZE feature detector K-means clustering MATLAB software Vision System Toolbox and Statistics and Machine Learning Toolbox , 321 , Top choice 91.5% ± 3.8% 2 choice 97.1% ± 2.0% 3 choice 98.4% ± 1.3% , - Limited number of available hardware systems for training. - Additional datasets are needed to evaluate visual artifacts and overlapping radiopaque “noise.” - Prospective data is required to assess the clinical utility of the model. - Potential applications of hardware classification beyond revision ACDF surgery..

Techniques: Biomarker Discovery, Comparison, Fluorescence, Imaging, Raman Spectroscopy, Microscopy, Extraction, Derivative Assay, Functional Assay, Infection, Software, Magnetic Resonance Imaging, Diffusion-based Assay